Forest, Fire, and Disturbance · Forest, Fire, and Disturbance PCB 3044L The effects of prescribed...
Transcript of Forest, Fire, and Disturbance · Forest, Fire, and Disturbance PCB 3044L The effects of prescribed...
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The effects of prescribed fire in the Pine Flatwoods ecosystem of the UCF Arboretum
Leigh Mingledorff
Abstract I examined the effects of prescribed fire on one section of the arboretum
(Unit 1) and compared it to an unburned section (East). Each of six
groups utilized the point-quarter sampling method in both Unit 1 and
East sections to measure the distances to canopy, sub-canopy, shrub
and dead log. Groups also measured the DBH of canopy, sub-canopy,
and dead logs. Utilizing this collected data, we were able to make
individual calculations including the average point-to-plant, average
area per plant, average densities, frequencies, and coverage
percentages for both Unit 1 and the East section. Finally, importance
values were calculated to demonstrate which species for canopy and
sub-canopy were the most influential. After examining my data and
figures, I found that the prescribed burn is useful in promoting diversity,
clearing the area of excess litter, reducing fuel and is all around
necessary for managing the pine flatwoods ecosystem.
Introduction Being fire-dependent, pine flatwoods require regular fire exposure to
properly maintain their ecosystem. Fires are important to pine flatwoods
and scrub habitats because litter is burned, fuel is reduced, nutrients
are cycled, and hardwood competitors are suppressed. Certain species
of plants require fire in order to release seeds for dispersal and
germination, particularly among pines (Flatwoods). In addition, various
species of animals, some of which are endangered, rely on the pine
flatwoods habitat for survival. Following a study performed by Martin B.
Main and Larry W. Richardson, in less than six months after a prescribed
fire, wildlife use of pine flatwoods increased (Main and Richardson).
Prescribed fires are planned in Florida “to maintain fire dependent
ecosystems and restore those outside their natural balance (Prescribed
Fire)”. The first prescribed burn in UCF history was in May of 2005 in
Unit 1. Further burns have been planned and carried out. Each ecology
lab group collected data on the areas Unit 1 and East. When compared
to Unit 1 and given that the East section was/is unburned, I predicted
that on average, the East section would have shorter distances to
canopy, sub-canopy and shrub vegetation. The burned section, Unit 1,
would have shorter distances to dead logs. I also predict that the East
section would have higher importance values among species of oaks
due to the lack of burns to remove the hardwood vegetation and that
Unit 1 would have higher importance values among pines.
Materials and Methods Each of six ecology lab groups of four to five people were given 3-4
yardsticks, a field bag containing a compass, a distance measuring tape
(in meters), a DBH or diameter at breast height measuring tape (in π
centimeters), and special tape to mark each individual area that data
collection occurred. Our data collection was in the form of point-quarter,
which is the most popular plotless sampling method and is sensitive to
non-random distributions. My group and I selected a random starting
point in the East section and marked it with our ruler, noting the cardinal
directions and dividing the area into four quadrants. For each quadrant
we measured and recorded the distance (in meters) from the center of
the marker to the center of the nearest canopy vegetation, sub-canopy
vegetation, shrub vegetation, and dead log. We measured the DBH (in π
centimeters) of the canopy, sub-canopy, and dead log. In addition we
identified the live species that were sampled and gave percentage
cover for grasses, woody plants, annuals/perennials, and open space
around the center of the marker. After all four quadrants of data for our
first sample point was collected, we marked our location with tape and
moved along in one direction to randomly choose another point when
we were out of our first sample range. We repeated the previous steps
three more times for East before sampling three more points the same
way in Unit 1.
ResulFigurefor can
As see
for can
differe
shrubs
the Eas
section
0
2
4
6
8
10
12
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Met
ers
(m)
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ub were lo
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ub
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logs in
in Unit
2
4
6
8
10
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16
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ant in Unit
ve, the ave
iceably la
as 49.98m
e average
more area
than dou
larger.
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rea Per
t 1 and Ea
erage are
arger in U
m2 for can
es for shru
a. Lastly, t
uble the ar
rub
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ast for can
ea per pla
Unit 1 than
nopy, 77.5
ubs were
the area a
rea aroun
Dead Log
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ant for
n in East.
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e extreme
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Unit 1
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2
5
7
10
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15
17
20
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nts
/ H
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ree 3.0: Totaanopy, shr
comparin
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al density
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y, 63.98 p
ubs. Unit
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ll, I found
ons.
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ubs occup
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and shrub
he
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nts/ hecta
average
higher).
py the
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opy,
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Discussion When regarding my calculations, I find that part of my original
hypothesis was correct and part was incorrect. When compared to the
East section, Unit 1 had longer average distances, larger average areas,
and less total density for canopy, sub-canopy, and shrub vegetation. The
East section had longer average distances and larger average areas for
dead logs. I attribute the longer average distances, larger average
areas, and less total density for canopy, sub-canopy, and shrubs of Unit
1 to the prescribed fire of May 2005, which cleared out Unit 1 of any
excess vegetation, leaving more room around each plant and less
vegetation to contribute towards total density. In addition I believe that
the prescribed fire of 2005 is responsible for the shorter average
distances to dead logs as the burn most likely aided the death of weaker
vegetation. I found that when comparing the summed importance values
for sub-canopy Oak and Pine genera, that surprisingly Unit 1 has a
higher value for Oak species and East has a higher value for Pine
species. However, the groundcover percentage estimations measured
inside the marker’s center area show that the East section had a higher
percentage of woody groundcover (40.1%) and that Unit 1 had a higher
percentage of herbaceous plants (38.9%). That would mean that
because the East has yet to be burned, that the woody trees are coming
in and establishing without a fire to keep their seedlings suppressed.
The lack of prescribed fire for the East section of the arboretum has
affected the pine flatwoods ecosystem there by letting vegetation grow
unchecked. Thus there is a larger density of vegetation. The East section
had a smaller amount of species listed than Unit 1 for sub-canopy and
shrub, indicating the East section has less diversity. Influence-wise,
Slash Pine (Pinus elliottii) is the dominant (and only species listed) for
canopy plants and Sand Pine (Pinus clausa) has the leading importance
value for sub-canopy. Otherwise, Saw Palmetto (Serenoa repens) had the
highest absolute density.
Unit 1, when compared to the East section, is characterized by longer
average distances point-to-plant, larger mean areas around vegetation,
and less overall density of vegetation. Saw palmetto (Serenoa repens)
has the highest absolute density. Slash Pine (Pinus elliottii) has the
highest importance value for canopy plants and Sabal Palm (Sabal
palmetto) has the highest value for sub-canopy. Unit 1 had a larger
amount of species listed than the East section for sub-canopy and shrub,
indicating more diversity among species is present in the burned area.
After reviewing my calculations and graphs I created, I am not overly
confident in the validity of the data. Considering I did not collect this
data alone or with a professional organization, I am not certain whether
my classmates correctly identified each plant or even correctly
measured each plant’s distance or DBH. When reviewing the raw data
for Unit 1 sub-canopy, I see that one listing of Sabal Palmetto was listed
to have a DBH of 47.5, which would have been a mature tree as mature
heights differ between diameters of 30-61 cm (Wade and Langdon). In
addition, approximately 42% of sampled points from Unit 1 sub-canopy
were discarded and not entered due to their distance being over 50
meters from the center of the marker. The sub-canopy trees located
outside of the 50 meter distance could have significantly altered my
calculations, were measurements given for their DBH. Otherwise, I
believe leaving out that 42% of sub-canopy trees were more than 50
meters away affected the mean distance point-to-plant, making the
average for sub-canopy distances smaller than it should have been.
With regard to Unit 1, based on the longer mean point-to-plant
distances, the larger average areas per plant, and the smaller amount of
density occupied but with more diversity, I strongly believe prescribed
burns are most definitely a useful tool in managing the UCF arboretum.
In addition, the knowledge that fire is essential to maintain the pine
flatwoods ecosystem supports my conclusion.
As far as recommendations go, I am unsure of the previous lengths of
preparation for prescribed burns on the UCF campus, however I did
note that some of the trees in Unit 1 had sustained fire damage. When
planning a fire, considering the weather is one of the most important
things to do in terms of a successful burn and proper smoke
management (Wade). It is obvious that there needs to be a plan laid out
to carry out the prescribed fire properly, when burning the fire must be
monitored for any irregularities, and a backup plan in case anything
goes awry. Evidence suggests that “a single fire may not be effective in
restoring flatwoods (Abrahamson)”. Therefore, planning for future burns
for other areas of the arboretum, including additional future burns of
Unit 1 is key.
Appendix Table 1.0: Equations 1-3 for Unit 1 and East
Unit 1 East
Eqn. 1: Mean Point‐to‐Plant Distance
Canopy 12.856 10.784
Sub‐Canopy 12.470 8.829
Shrub 2.228 2.187
Dead Log 7.184 11.311
Eqn. 2: Mean Area Per Plant
Canopy 165.267 116.288
Sub‐Canopy 155.507 77.951
Shrub 4.964 4.783
Dead Log 51.612 127.948
Eqn.3: Total Density
Canopy 60.508 85.994
Sub‐Canopy 64.306 128.285
Shrub 2014.511 2090.775
Dead Log 193.752 78.157
Table 2.0: Equations 4 and 5 for Unit 1
Unit 1 Species Eqn. 4: Relative
Density Eqn. 5: Absolute
Density
Canopy Pinus elliottii 1.000 60.508Sub‐Canopy Gordonia lasanthus 0.024 1.531
Myrica cerifera 0.048 3.062
Pinus clausa 0.048 3.062
Pinus elliottii 0.190 12.249
Quercus chapmanii 0.214 13.780
Quercus geminata 0.024 1.531
Quercus myrtifolia 0.214 13.780
Quercus virginiana 0.119 7.655
Rhus coppalinum 0.024 1.531
Sabal palmetto 0.095 6.124Shrub Andropogon capillipes 0.013 25.181
Asimina reticulata 0.013 25.181
Baccharis halimifolia 0.013 25.181
Befaria racemosa 0.013 25.181
Ilex glabra 0.038 75.544
Lyonia ferruginea 0.025 50.363
Lyonia lucida 0.188 377.721
Lyonia mariana 0.125 251.814
Myrica cerifera 0.013 25.181
Pinus elliottii 0.013 25.181
Quercus chapmanii 0.013 25.181
Quercus geminata 0.013 25.181
Quercus myrtifolia 0.038 75.544
Sabal palmetto 0.013 25.181
Serenoa repens 0.388 780.623
Table 2.1: Equations 4 and 5 for East
East Species Eqn. 4: Relative
Density Eqn. 5: Absolute
Density
Canopy Pinus elliottii 1.000 85.994Sub‐Canopy Myrica cerifera 0.028 3.614
Pinus clausa 0.521 66.853
Pinus elliottii 0.099 12.648
Pinus serotina 0.014 1.807
Quercus chapmanii 0.014 1.807
Quercus geminata 0.028 3.614
Quercus myrtifolia 0.239 30.716
Quercus virginiana 0.056 7.227Shrub Asimina reticulata 0.048 100.760
Befaria racemosa 0.024 50.380
Ilex glabra 0.060 125.950
Licania michauxii 0.012 25.190
Lyonia lucida 0.241 503.801
Lyonia mariana 0.096 201.520
Pinus clausa 0.012 25.190
Quercus chapmanii 0.048 100.760
Quercus myrtifolia 0.012 25.190
Quercus virginiana 0.036 75.570
Serenoa repens 0.410 856.462
Table 3.0: Equations 6 and 7 for Unit 1
Unit 1 Species Eqn. 6: Frequency Eqn. 7: Relative
Frequency
Canopy Pinus elliottii 1.000 1.000Sub‐Canopy Gordonia lasanthus 0.048 0.029
Myrica cerifera 0.095 0.059
Pinus clausa 0.095 0.059
Pinus elliottii 0.286 0.176
Quercus chapmanii 0.286 0.176
Quercus geminata 0.048 0.029
Quercus myrtifolia 0.381 0.235
Quercus virginiana 0.238 0.147
Rhus coppalinum 0.048 0.029
Sabal palmetto 0.095 0.059Shrub Andropogon capillipes 0.048 0.020
Asimina reticulata 0.048 0.020
Baccharis halimifolia 0.048 0.020
Befaria racemosa 0.048 0.020
Ilex glabra 0.095 0.039
Lyonia ferruginea 0.095 0.039
Lyonia lucida 0.429 0.176
Lyonia mariana 0.286 0.118
Myrica cerifera 0.048 0.020
Pinus elliottii 0.048 0.020
Quercus chapmanii 0.238 0.098
Quercus geminata 0.048 0.020
Quercus myrtifolia 0.143 0.059
Sabal palmetto 0.048 0.020
Serenoa repens 0.762 0.314
Table 3.1: Equations 6 and 7 for East
East Species Eqn. 6: Frequency Eqn. 7: Relative
Frequency
Canopy Pinus elliottii 1.000 1.000Sub‐Canopy Myrica cerifera 0.095 0.054
Pinus clausa 0.667 0.378
Pinus elliottii 0.238 0.135
Pinus serotina 0.048 0.027
Quercus chapmanii 0.048 0.027
Quercus geminata 0.048 0.027
Quercus myrtifolia 0.476 0.270
Quercus virginiana 0.143 0.081Shrub Asimina reticulata 0.190 0.070
Befaria racemosa 0.095 0.035
Ilex glabra 0.143 0.053
Licania michauxii 0.048 0.018
Lyonia lucida 0.762 0.281
Lyonia mariana 0.238 0.088
Pinus clausa 0.048 0.018
Quercus chapmanii 0.190 0.070
Quercus myrtifolia 0.048 0.018
Quercus virginiana 0.143 0.053
Serenoa repens 0.810 0.298
Table 4.0: Equations 8-10 for Unit 1
Unit 1 Species Eqn. 8: Coverage Eqn. 9: Relative
Coverage Eqn. 10:
Importance Value
Canopy Pinus elliottii 37968.508 1.000 3.000Sub‐Canopy Gordonia lasanthus 0.301 0.000 0.053
Myrica cerifera 32.047 0.005 0.111
Pinus clausa 41.571 0.006 0.113
Pinus elliottii 334.287 0.049 0.416
Quercus chapmanii 128.477 0.019 0.410
Quercus geminata 10.113 0.001 0.055
Quercus myrtifolia 245.710 0.036 0.486
Quercus virginiana 286.331 0.042 0.308
Rhus copallinum 5.820 0.001 0.054
Sabal palmetto 5701.728 0.840 0.994
Table 4.1: Equations 8-10 for East
East Species Eqn.8: Coverage Eqn.9: Relative
Coverage Eqn.10:
Importance Value
Canopy Pinus elliottii 32443.026 1.000 3.000Sub‐Canopy Myrica cerifera 60.765 0.019 0.101
Pinus clausa 2401.707 0.757 1.657
Pinus elliottii 221.717 0.070 0.304
Pinus serotina 59.956 0.019 0.060
Quercus chapmanii 0.128 0.000 0.041
Quercus geminata 5.974 0.002 0.057
Quercus myrtifolia 262.548 0.083 0.592
Quercus virginiana 158.596 0.050 0.187
Table 5.0 Average percentage groundcover recorded within the center of the marker
Average Percentage Groundcover
Unit 1 East
Grass 38.9 27.7
Woody 32.4 40.1
Annuals/ Perennials 3.9 4.0
Open Space 24.8 27.2
Literature Cited Abrahamson, Warren G., and Christy R. Abrahamson. "Effects of Fire on Long-
Unburned Florida Uplands." Journal of Vegetation Science 7 (1996): 565-74.
JSTOR. 30 Nov. 2008 <http://www.jstor.org/stable/3236306>.
"Flatwoods." Florida 4-H Forest Ecology. University of Florida. 30 Nov. 2008
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